RE: st: gologit2

While I find this entire discussion extremely interesting and useful
for my research, I don't yet find a solution for how to handle
referees who often can be mechanical and rigid about the departures
they will allow from the most conservative textbook practices.

The question of how one would get an analysis based on an ordinal
estimator into a strong journal if it doesn't pass the Brant test,
unfortunately still remains unanswered. While some of the advice has
been invaluable, I need statistical cites for these suggested
departures from this test. The Scott Long book on this subject
(Long, J. Scott. 1997. Regression Models For Categorical and Limited
Dependent Variables. Thousand Oaks CA:Sage) won't be of any help
because he simply states that ordinal analyses often won't pass the test.

Can anyone offer statistical or econometric cites that justify the
Brant test substitutes that people have mentioned on the list?

Thanks.

Dave Jacobs

At 03:04 PM 4/17/2008, you wrote:

In an earlier response in this thread,
Richard Williams <Richard.A.Williams.5@ND.edu> remarked:

>My experience is that it is rare to have a model where the
>proportional odds assumption isn't violated! Often, though, the
>violation only involves a small subset of the variables, in which
>case gologit2 can be useful. You might also want to consider more
>stringent alpha levels (e.g. .01, .001) to reduce the possibility of
>capitalizing on chance. You can also try to assess the practical
>significance of violations, e.g. do my conclusions and/or predicted
>probabilities really change that much if I stick with the model whose
>assumptions are violated as opposed to a (possibly much harder to
>understand and interpret) model whose assumptions are not violated.

I would sound in to support the idea that the Brant test commonly
detects departures from proportional odds that are so small as to be
uninteresting. In fact, I would suggest as a conjecture that, if
the sample size
is large enough to trust the asymptotic p-values from the Brant
test, then the sample size is large enough that trivial departures
from prop. odds will achieve small p-values. I would suggest
instead approaching this specification problem by looking at the
relative increase in the pseudo-R^2 value associated with moving to
a non-proportional odds model. My own experiments on using such
measures to address the related problem of variable choice ordinal
logit models shows that one measures is about as good as the next.
(see my comment in
http://www.stata.com/statalist/archive/2008-03/msg00249.html for a
brief discussion of this point and a citation.)
Now, I admit that there is a problem in knowing exactly how big a
*relative* change in R^2 (10%?) warrants a more complicated model,
but I don't think this is worse than to p-values as the sole arbiter.